Produce complete AI videos from a brief with Maestro
Produce complete videos from a natural-language brief with Maestro through the Ace Data Cloud API. Maestro plans the script, creates or sources media, generates voiceover and music, edits, captions, renders, and returns finished video variants.
pip install mcp-maestro
export ACEDATACLOUD_API_TOKEN="your-token"
mcp-maestro
Get an API token from platform.acedata.cloud.
For a hosted connection, use https://maestro.mcp.acedata.cloud/mcp. It accepts a direct Ace Data Cloud Bearer token and supports OAuth sign-in.
| Tool | Purpose |
|---|---|
maestro_create_video | Create a video or run remix, edit, or extend on an earlier task |
maestro_get_task | Read progress, status, and final language variants for one task |
maestro_list_tasks | List the authenticated account's recent tasks, newest first |
Ask an MCP client:
Create a 45-second 16:9 English product launch video from this product photo. Use an editorial style and a documentary voice.
The tool returns a task_id immediately. Query that ID until status is succeeded or failed. Successful tasks expose videos in response.data.variants.
To inspect existing task history without creating a video, call maestro_list_tasks. It accepts a limit from 1 to 100 and optional exclusive created_at_min / created_at_max Unix timestamp bounds. The returned items honor those filters; count remains the authenticated account's total visible task count.
Maestro provides the complete capability set on every request: all actions and scenarios, 5–300 seconds, up to 4 languages, and 1080p/30fps output. The base price is 0.60 Credits per delivered second. Avatar uses a 1.15× scenario multiplier, drama uses 1.35×, and each additional delivered language adds 6 Credits. Failed tasks and task polling are free.
To revise an existing result, call maestro_create_video with an iteration action and the prior task ID:
{
"prompt": "Keep the visuals but tighten the first 10 seconds and use a warmer voice.",
"action": "edit",
"ref_task_id": "previous-task-id"
}
{
"mcpServers": {
"maestro": {
"command": "uvx",
"args": ["mcp-maestro"],
"env": {
"ACEDATACLOUD_API_TOKEN": "your-token"
}
}
}
}
pip install -e ".[dev,test,release]"
pytest --cov=core --cov=tools
ruff check .
ruff format --check .
mypy core tools main.py
python -m build
See the Maestro API documentation for billing and response details.
Source-derived launch command. Check the maintainer’s required arguments and credentials before running:
uvx mcp-maestroMerge this template into ~/Library/Application Support/Claude/claude_desktop_config.json. Keep existing servers. Add any arguments, credentials, and permissions required by the maintainer; this template has not been install-tested.
{
"mcpServers": {
"io-github-acedatacloud-mcp-maestro": {
"command": "uvx",
"args": [
"mcp-maestro"
]
}
}
}Restart Claude Desktop completely for changes to take effect. Confirm the server appears connected in the client’s tool list, then try a read-only example from its documentation.
Claude Desktop setup referencemcp-maestropypiio.github.AceDataCloud/mcp-maestro works with any MCP-compatible client. Copy the config snippet from the Configuration section above and add it to the file shown for your client, then restart the application.
~/Library/Application Support/Claude/claude_desktop_config.jsonRestart Claude Desktop completely for changes to take effect.~/.cursor/mcp.jsonRestart Cursor for changes to take effect..vscode/mcp.jsonReload VS Code window for changes to take effect.~/.codeium/windsurf/mcp_config.jsonRestart Windsurf for changes to take effect..mcp.jsonSave at the project root, then start Claude Code in that project and review the MCP server approval prompt. Keep real credentials out of shared files.